When a signal is decomposed into frequency bands, demodulated into modulator and carrier pairs, and portrayed in a carrier frequency-versus modulator frequency domain, significant information may be automatically observed about the signal. We refer to this domain as the modulation spectral domain. The modulation spectrum is referred to as a windowed Fourier transform across time that produces an acoustic frequency versus modulation frequency representation of a signal. Previously, frameworks incorporating the discrete short-time modulation transform (DSTMT) and modulation spectrum have been designed mostly for filtering of speech signals. This modulation spectral domain is rarely, if ever, discussed in typical signal processing courses today, and we believe its current associated tools and applications are somewhat limited. We seek to revisit this domain to uncover more intuition, develop new concepts to extend its capabilities, and increase its applications, especially in the area of music data mining. A recent interest has risen in using modulation spectral features, which are features in the modulation spectral domain, for music data mining. The field of music data mining, also known as music information retrieval (MIR), has been rapidly developing over the past decade or so. One reason for this development is the aim to develop frameworks leveraging the particular characteristics of music signals instead of simply copying methods previously applied to its speech-centered predecessors, such as speech recognition, speech synthesis, and speaker identification. This research seeks to broaden the perspective and use of an existing modulation filterbank framework by exploiting modulation features well suited for music signals. The objective of this thesis is to develop a framework for extracting modulation spectral features from music and other signals. The purpose of extracting features from these signals is to perform data mining tasks, such as unsupervised source identification, unsupervised source separation, and audio synthesis. More specifically, this research emphasizes the following: the usefulness of the DSTMT and the modulation spectrum for music data mining tasks; a new approach to unsupervised source identification using modulation spectral features; a new approach to unsupervised source separation; a newly introduced analysis of FM features in an AM-dominated modulation spectra; and other applications.

Modern speech communication technologies expose users to perceptual quality degradations that were not experienced earlier with conventional telephone systems. Since perceived speech quality is a major contributor to the end user's perception of quality of service, speech quality estimation has become an important research field. In this dissertation, perceptual quality estimators are proposed for several emerging speech communication applications, in particular for i) wireless communications with noise suppression capabilities, ii) wireless-VoIP communications, iii) far-field hands-free speech communications, and iv) text-to-speech systems. First, a general-purpose speech quality estimator is proposed based on statistical models of normative speech behaviour and on innovative techniques to detect multiple signal distortions. The estimators do not depend on a clean reference signal hence are termed ``blind." Quality meters are then distributed along the network chain to allow for both quality degradations and quality enhancements to be handled. In order to improve estimation performance for wireless communications, statistical models of noise-suppressed speech are also incorporated. Next, a hybrid signal-and-link-parametric quality estimation paradigm is proposed for emerging wireless-VoIP communications. The algorithm uses VoIP connection parameters to estimate a base quality representative of the packet switching network. Signal-based distortions are then detected and quantified in order to adjust the base quality accordingly. The proposed hybrid methodology is shown to overcome the limitations of existing pure signal-based and pure link parametric algorithms. Temporal dynamics information is then investigated for quality diagnosis for hands-free speech communications. A spectro-temporal signal representation, where speech and reverberation tail components are shown to be separable, is used for blind characterization of room acoustics. In particular, estimators of reverberation time, direct-to-reverberation energy ratio, and reverberant speech quality are developed. Lastly, perceptual quality estimation for text-to-speech systems is addressed. Text- and speaker-independent hidden Markov models, trained on naturally produced speech, are used to capture normative spectral-temporal information. Deviations from the models, computed by means of a log-likelihood measure, are shown to be reliable indicators of multiple quality attributes including naturalness, fluency, and intelligibility. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2008-12-22 14:54:49.28

This diploma thesis deals with the topic of the ECG quality evaluation. The theoretical part of the thesis contains an overview of the methods, which were studied and an explanation of the basic principles connected with the quality evaluation of the ECG signals. The practical part deals with the implementation of three selected methods, one of which is the continuous evaluation of signal quality by means of SNR (signal to noise ratio) calculation. The results of these methods are further discussed and compared.

The aim of master’s thesis is a classification of digital modulation type. The interest in modulation classification has been growing for last years. It has several possible roles in both civilian and military applications such as spectrum sensing, signal confirmation, interference identification, monitoring and so on. Modulation classification is an intermediate step between signal detection and successful demodulation. Therefore the known methods are based on different statistics obtained from received signals. These statistics can be derived from continuous time signals and they hold for sampled signals.

This work deals with a design of hypokinetic dysarthria analysis system. Hypokinetic dysarthria is a speech motor dysfunction that is present in approx. 90 % of patients with Parkinson’s disease. The work is mainly focused on parameterization techniques that can be used to diagnose or monitor this disease as well as estimate its progress. Next, features that significantly correlate with subjective tests are found. These features can be used to estimate scores of different scales like Unified Parkinson’s Disease Rating Scale (UPDRS) or Mini–Mental State Examination (MMSE). A protocol of dysarthric speech acquisition is introduced in this work too. In combination with acoustic analysis it can be used to estimate a grade of hypokinetic dysarthria in fields of faciokinesis, phonorespiration and phonetics (correlation with 3F test). Regarding the parameterization, features based on modulation spectrum, inferior colliculus coefficients, bicepstrum, approximate and sample entropy, empirical mode decomposition and singular points are originally introduced in this work. All the designed techniques are integrated into the system concept in way that it can be implemented in a hospital and used for a research on Parkinson’s disease or its evaluation.